Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 022

Tail Event Driven Factor Augmented Dynamic Model

Weining Wang
Lining Yu
Bingling Wang

A factor augmented dynamic model for analysing tail behaviour of high
dimensional time series is proposed. As a first step, the tail event driven
latent factors are extracted. In the second step, a VAR (Vectorautoregression
model) is carried out to analyse the interaction between these factors and the
macroeconomic variables. Furthermore, this methodology also provides the
possibility for central banks to examine the sensitivity between macroeconomic
variables and financial shocks via impulse response analysis. Then the
predictability of our estimator is illustrated. Finally, forecast error variance
decomposition is carried out to investigate the network effect of these
variables. The interesting findings are: firstly, GDP and Unemployment rate are
very much sensitive to the shock of financial tail event driven factors, while
these factors are more affected by inflation and short term interest rate.
Secondly, financial tail event driven factors play important roles in the
network constructed by the extracted factors and the macroeconomic variables.
Thirdly, there is more connectedness during financial crisis than in the stable
periods. Compared with median case, the network is more dense in lower quantile

Quantile Regression, Expectile Regression, Dynamic Factor Model, Dynamic Network

JEL Classification:
C21, C51, G01, G18, G32, G38